Product auditing in point-of-sale images

ABSTRACT

Examples methods, apparatus/systems and articles of manufacture for auditing point-of-sale images are disclosed herein. Example methods disclosed herein include comparing a region of interest of an image displayed via a user interface with a plurality of reference product images stored in a database to identify a plurality of candidate product images from the plurality of reference product images as potential matches to a first product depicted in the image. For example, the candidate product images are associated with respective confidence levels indicating respective likelihoods of matching the first product. Disclosed example methods also include displaying, via the user interface, the candidate product images simultaneously with the image in a manner based on the respective confidence levels, and automatically selecting a first one of the candidate product images as matching the first product based on the respective confidence levels.

FIELD OF THE DISCLOSURE

This disclosure relates generally to point-of-sale images and, moreparticularly, to product auditing in point-of-sale images.

BACKGROUND

Image recognition may be used to identify consumer packaged goods for avariety of purposes. For example, when performing an in-store shelfaudit, a market research firm may use an image recognition applicationto identify the products displayed on store shelves. As another example,a merchant may offer an image recognition application to consumers tofacilitate comparison shopping. In some examples, image recognitionapplications or programs attempt to identify products shown in picturesor images of a shelf taken at a point-of-sale. After the imagerecognition application or program has analyzed the point-of-sale image,an auditor manually reviews the results to verify the accuracy and/ormake corrections. An auditor typically has to adjust or modifyinformation in the results.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 illustrates an example auditing system constructed in accordancewith the teaching of this disclosure to perform product auditing inpoint-of-sale images.

FIG. 2A illustrates an example segmented image generated by an exampleautomatic image recognition application of the example auditing systemof FIG. 1.

FIG. 2B illustrates an example of the segmented image of FIG. 2A inwhich products were not recognized and/or products are mislabeled by theexample automatic image recognition application.

FIGS. 2C and 2D illustrate an example operation to identify an exampleregion of interest in the segmented image of FIG. 2B.

FIG. 2E illustrates an example frame generated around the region ofinterest identified in FIGS. 2C and 2D and produced by the exampleauditing system of FIG. 1.

FIG. 3 illustrates an example quick preview window displaying examplecandidate products that may correspond to a product in the region ofinterest identified in FIGS. 2C and 2D.

FIG. 4 illustrates another example quick preview window for displayingproduct information related to a product identified in a frame of asegmentation and generated by the example auditing system of FIG. 1.

FIG. 5 illustrates an example implementation of an example candidateproduct searcher of the example auditing system of FIG. 1 that may beused to identifying the candidate products displayed in the examplequick preview window of FIG. 3.

FIG. 6 is a flowchart representative of an example method, at leastpartially executable by machine readable instructions, which may beimplemented by the example auditing system of FIG. 1.

FIG. 7 is a flowchart representative of an example method, at leastpartially executable by machine readable instructions, to perform anexample confidence search, which may be implemented by the examplecandidate product searcher of FIG. 5.

FIG. 8 is a flowchart representative of an example method, at leastpartially executable by machine readable instructions, which may beimplemented by the example auditing system of FIG. 1.

FIG. 9 is a block diagram of an example processor system structured toexecute example machine readable instructions represented by FIGS. 6, 7and/or 8 to implement the example auditing system of FIG. 1.

FIG. 10 is a block diagram of an example processor system structured toexecute example machine readable instructions represented by FIG. 7 toimplement the example candidate product searcher of FIGS. 1 and 5.

DETAILED DESCRIPTION

Disclosed herein are example auditing methods, apparatus/systems andarticles of manufacture that may be implemented to perform productauditing of a point-of-sale image. Example systems and methods disclosedherein can significantly reduce the amount of human interventionrequired to review results of an automatic image recognition engine.Disclosed example systems and methods enable a user to identify a regionof interest (ROI) in a segmentation of an image containing a productthat has not been recognized by the automatic image recognition engine.As used herein, an ROI is an area, a portion, a location, etc. of animage depicting, or identified as possibly depicting, one or moreproduct(s) and/or other item(s), element(s), etc. of interest. Suchdisclosed example systems and methods produce a list of candidateproducts with assigned confidence levels. With this information, anauditor can quickly determine which product is to be matched or assignedwith the product in the ROI. The disclosed example systems and methodsalso enable an auditor to quickly modify or adjust product informationassociated with a product detected by the automatic image recognitionapplication, further reducing the amount of time required for reviewingand verifying auditing information generated by an automatic imagerecognition engine. As a result, the disclosed example systems andmethods can significantly reduce labor costs associated with the processof manually identifying and correcting unrecognized and/or incorrectlylabeled products in an image processed by an automatic image recognitionengine.

In general, market research entities (MREs) and/or merchants (e.g.,wholesalers, club stores, retailers, e-commerce retailers) offerservices based on recognizing consumer packaged goods (e.g., products,items, objects, etc.) from images captured by image capturing devices(e.g., cameras, smartphones, tablets, video recorders, etc.). Forexample, an MRE may provide point-of-sale (e.g., shelf) auditingservices to marketers, manufacturers, merchants and/or advertisers tohelp determine if, for example, display locations of products and/ordisplay quality requirements (e.g., the goods are facing the correctorientation, the shelf is fully stocked, etc.) are being met. MRE and/ormerchants typically use an automatic image recognition application(e.g., program, software, algorithm) that analyzes a point-of-sale imageand attempts to detect and identify the products in the point-of-saleimage.

Some prior automatic image recognition applications generate a segmentedimage, also referred to as a segmentation, which shows the locations andthe names of the products recognized by the automatic image recognitionapplication over the top (e.g., overlaid) the image. In some examples,the segmented image includes a plurality of frames (e.g., objects,squares, rectangles) overlaid on top of the image, which show (e.g.,indicate) the boundaries of the products detected in the image. Theframes include the names of the respective products detected during theautomatic image recognition process. As used herein, a frame is agraphical object (e.g., a shape, such as a rectangle, a square, acircle, an oval, etc., and/or a boundary, an icon, etc.) displayed overor otherwise with an image to indicate a location, region, etc. of arecognized product, item, element, etc. and/or an ROI in the image.

Although generally effective in most situations, some prior automaticimage recognition applications fail to detect all of the products in theimage and/or incorrectly identify products. As such, to ensure highaccuracy output, an auditor (e.g., a user, an operator, a technician,etc.) manually reviews the segmented image to verify that all of theproducts in the image have been detected and/or that all of the productsare correctly identified. In particular, this process may be separatedinto two tasks: (1) identifying regions of interest (ROI), which areareas where products that still need to be segmented appear (e.g., werenot detected by the automatic image recognition application), and (2)labeling the products in each ROI. In some examples, labeling theproducts involves manually searching through a database containingthousands of products and then assigning an item number (SKU) and/orInternational Article Number (EAN) code to the area once a referenceproduct is located. This process of manually reviewing thousands ofproducts in a database and assigning SKU/EAN codes requires significanttime and experience on behalf of the auditor.

Example auditing systems and methods disclosed here enable a MRE, forexample, to perform product auditing of a point-of-sale image. Inparticular, disclosed herein are example methods and systems that enablean auditor to more quickly and efficiently verify and update the resultsof a segmented image produced by an automatic image recognitionapplication. In some examples disclosed herein, an auditor may identify(e.g., highlight) a region of interest (ROI) (e.g., an area) in thesegmented image having one or more products that were not detected bythe automatic image recognition application. The example system detectsthe ROI identified by the auditor and performs a confidence search(e.g., via an algorithm), where the ROT is compared to reference imagesof products stored in a product database. The example auditing systemproduces or generates a list of candidate products that may be in theROI identified by the auditor. In some examples, the candidate productsare assigned confidence levels (e.g., a score, a value), which indicatethe likelihoods or degrees of certainty the respective candidateproducts are the same as the product in the ROI. In some examples, thecandidate products are displayed simultaneously as the ROI (e.g.,adjacent to the ROI and/or on top of the segmented image). The candidateproducts may be sorted by their receptive confidence levels. In someexamples, the candidate products are displayed in a window or userinterface that is activated by the auditor. For example, after theauditor identifies the ROI and the confidence search is performed, theauditor may select the ROI to view the candidate products in a quickpreview window adjacent the ROI. In some examples, the candidate producthaving the highest confidence level is automatically assigned (e.g.,matched) to the product in the ROI. However, if the candidate productassigned to the product in the ROI is incorrect, the auditor can easilyreview the other candidate products and determine (e.g., with a level ofconfidence or degree of certainty) which product is to be assigned tothe product in the ROI.

In some examples, the confidence search may be performed on an imagethat has not been analyzed by an automatic image recognitionapplication. For example, an auditor may desire to identify only a fewproducts in a point-of-sale image. Instead of waiting for the automaticimage recognition application to process the entire image (e.g., whichmay take a substantial amount of time), the auditor may identify aparticular ROI in the image. In such an example, the auditing systemperforms a confidence search to identify one or more candidate products,assigns confidence level(s) to the candidate product(s), and displaysthe candidate product(s) to the auditor. This abbreviated process isrelatively fast compared to a full automatic image recognition processthat analyzes the entire image. Additionally, by displaying thecandidate products with their confidence levels (e.g., or by sorting thecandidate products by their confidence levels), the example systems andmethods enable relatively new or less experienced auditors to moreaccurately and efficiently review a segmented image. Therefore, theexample system and methods reduce the skill or experience required byauditors reviewing segmented images.

An example confidence search disclosed herein includes detecting cornersof a region of interest. In some examples, an optimal acceleratedsegment test (OAST) is used to detect the corners. The exampleconfidence search includes extracting features from the detected cornersof the region of interest. To compare the region of interest against areference product image (e.g., a pattern, a captured image, etc.) of areference product, the confidence search also detects corners of thereference image and extracts features from the corners of the referenceimage. The example confidence search compares the features of the regionof interest and the features of the reference image to determinesimilarities or relationships between the features. Once therelationships between the features are obtained, a dominanttransformation projection (e.g., a mathematical formula or function) isidentified that can transform the features from the region of interestto the features of the reference image, or vice versa. In some examples,the dominant transformation projection represents the most important orinfluential match/relationship between the features of the region ofinterest and the features of the reference image. In some examples, theconfidence search then creates or calculates a confidence level (e.g., ascore, a value) for the reference product represented by the referenceproduct image. In some examples, the confidence level is based on anumber of inliers (i.e., the number of features between the ROI and thereference product image that are compatible with the relationship). Inother words, the more inliers captured, the higher the confidence level.This confidence search process may be repeated for other referenceimages associated with the same and/or other reference products storedin a product database. In some examples, after analyzing multiplereference products, the reference products are sorted by the confidencelevels and presented to the auditor as candidate products. In someexamples, only reference products having confidence levels above athreshold are considered candidate products.

In some examples, as mentioned above, an automatic image recognitionapplication may incorrectly label a product in a segmented image. Todecrease the amount of effort spent by an auditor correcting the label,example auditing systems disclosed herein enable an auditor to select alabeled product and display a list of candidate products. In someexamples, the list of candidate products is displayed in a quick previewwindow, which is displayed when an auditor selects a particular frame ofa product in the segmented image. The auditor can then easily select adifferent product, which is then matched or assigned to the product inthe segmented image. In some examples, the candidate products are basedon reference products identified during the automatic recognitionprocess as potentially matching the corresponding product.

Additionally or alternatively, in some examples, the quick previewwindow may display product information associated with a product in aframe in the segmented image selected by the auditor. The selected framemay have been generated by an example automatic image recognitionapplication or by the auditor via an example ROI identification processdisclosed herein. The example quick preview window displays productinformation associated with the product identified in the particularframe. The product information may include an image (e.g., a thumbnail)of the recognized product, a type or name of the recognized product, aview or position (e.g., frontal, side, rear, etc.) of the recognizedproduct, a layout of the recognized product (e.g., a number of rows by anumber of columns), metric warnings with respect to the recognizedproduct (e.g., problems with the segmentation), an indication of whetherthe recognized product is in stock, an indication of whether therecognized product is on a pallet, a price of the recognized product, arange of the price of the recognized product, etc. The productinformation may be changed by the auditor if it is incorrect, which thenupdates the record corresponding to the product in the particular frame.In some examples, the quick preview window displays a listing of thecandidate products associated with the product in the selected frame. Insome examples, the quick preview window includes a search bar thatenables an auditor to search the product database by typing a word(e.g., a name of a product, a brand, a type of product) into the searchbar.

Example methods disclosed herein include comparing a region of interestof a first image displayed via a user interface with reference productimages stored in a database to identify candidate product images fromthe reference product images as potential matches to a first productdepicted in the first image. In such examples, the candidate productimages are associated with respective confidence levels indicatingrespective likelihoods of matching the first product. Such disclosedexample methods also include displaying, via the user interface, thecandidate product images simultaneously with the first image in a mannerbased on the respective confidence levels. Such disclosed examplemethods further include automatically selecting a first one of thecandidate product images as matching the first product based on therespective confidence levels.

In some disclosed examples, comparing the region of interest with thereference product images includes detecting first corners of the regionof interest, extracting first features from the detected first cornersof the region of interest, detecting second corners of a first referenceproduct image of the reference product images, extracting secondfeatures from the detected second corners, determining relations betweenthe first features and the second features, and determining a dominanttransformation projection between the region of interest and the firstreference product image based on the relations. In some such disclosedexamples, at least one of the first corners or the second corners isdetected using an optimal accelerated segment test. In some disclosedexamples, the dominant transformation projection is determined using arandom sample consensus (RANSAC) algorithm. Some disclosed examplemethods further include determining a number of inliers of the firstfeatures based on the dominant transformation projection and determininga first one of the respective confidence levels associated with thefirst reference product image based on the number of the inliers.

In some disclosed examples, comparing the region of interest of thefirst image with the reference product images to identify the candidateproduct images includes assigning the respective confidence levels tothe reference product images and comparing the respective confidencelevels associated with the reference product images to a threshold. Insome disclosed examples, the first image is a segmented image includingone or more frames corresponding to respective products identified inthe first image with an automatic image recognition process. In somesuch disclosed examples, the one or more frames includes respectiveproduct names overlaying the respective products identified in therespective frames.

Additionally or alternatively, some disclosed example methods furtherinclude at least one of creating a record or updating the record toindicate the first product matched the first one of the candidateproduct images. Some such disclosed example methods also includedisplaying product information corresponding to the first one of thecandidate product images. In some such disclosed examples, the productinformation includes at least one of a name of a reference productassociated with the first candidate reference image, a view or positionof the reference product, a layout of the reference product, anindication of whether the reference product is in stock, an indicationof whether the reference product is on a pallet or a price of thereference product.

In some disclosed examples, the candidate product images are displayedin order of the respective confidence levels. Some disclosed examplemethods further include displaying values representative of therespective confidence levels adjacent to the candidate product images.Some disclosed example methods also include displaying the first one ofthe candidate product images in a manner indicating the first one of thecandidate product images has been selected as being matched to the firstproduct.

Other example methods disclosed herein, additionally or alternatively,include displaying, via a user interface, a segmentation of an imagedepicting products, the segmentation generated by an automatic imagerecognition application. The segmentation includes a first frameoverlaying the image and defining a first location of a first productidentified by the automatic image recognition process. Such disclosedexample methods also include displaying, in response to a user selectionof the first frame, a first preview window simultaneously with the firstframe via the user interface. The first preview window includes firstproduct information associated with the first product. Such disclosedexample methods further include updating a first product recordassociated with the first product based on a modification made, via theuser interface, to the first product information displayed in the firstpreview window.

In some disclosed examples, the first product information includes atleast one of an image of the first product, a name of the first product,a view or position of the first product, a layout of the first product,an indication of whether the first product is in stock, an indication ofwhether the first product is on a pallet or a price of the firstproduct. In some such disclosed examples, the layout of the firstproduct includes a number of rows and columns of the first product. Insome disclosed examples, the modification is a change to at least one ofthe name of the first product, the view or position of the firstproduct, the layout of the first product, the indication of whether thefirst product is in stock, the indication of whether the first productis on a pallet or the price of the first product.

Some disclosed example methods further include displaying, via the firstpreview window, a first group of candidate reference products identifiedby the automatic image recognition application as potential matches tothe first product. In some disclosed examples, the modification is basedon a user selection of a first one of the first group of candidatereference products. In some such disclosed examples, updating the firstproduct record includes assigning the first one of the first group ofcandidate reference products as matching the first product. In somedisclosed examples, the segmentation further includes a second frameoverlaying the image and defining a second location of a second productidentified by the automatic image recognition process. Some suchdisclosed example methods also include displaying, in response to a userselection of the second frame, a second preview window simultaneouslywith the second frame. The second preview window includes second productinformation associated with the second product. Some such disclosedexample methods further include displaying, via the second previewwindow, a second group of candidate reference products identified by theautomatic image recognition application as potential matches to thesecond product.

These and other example methods, apparatus, systems and articles ofmanufacture (e.g., physical storage media) to implement product auditingin point-of-sale images are disclosed in further detail herein.

FIG. 1 illustrates an example auditing system 100 to perform imagerecognition and point-of-sale image auditing in accordance with theteachings of this disclosure. The example auditing system 100 may beimplemented by an MRE. For example, the auditing system 100 may be usedin a workflow of an auditor to perform product auditing in apoint-of-sale image. In the illustrated example, an example imagecapturing device 102 (e.g., a camera, a video recorder, a smartphone, atablet, etc.) captures an example image 104 (e.g., point-of-sale image)of an example shelf 106 or section of the shelf 106 having one or moreproducts 108 a-108 n (e.g., consumer packaged goods, items, objects,etc.). The example shelf 106 may be from a merchant (e.g., a retailer, awholesaler, a club store, etc.). In some examples, the shelf 106 may bein another location, such as a consumer's home, in a vending machine, ata kiosk, etc. In the illustrated example, the captured image 104 is sentto the auditing system 100 through an example network 110 (e.g., theInternet, a local area network, a wide area network, a cellular datanetwork, etc.) via a wired and/or wireless connection (e.g., acable/DSL/satellite modem, a cell tower, etc.).

In the illustrated example of FIG. 1, the auditing system 100 processesthe captured image 104 to identify the product(s) 108 a-108 n (and/orinformation associated with the product(s) 108 a-108 n) in the capturedimage 104 and, thus, perform an audit of the shelf 106 or section of theshelf 106. In the illustrated example, the auditing system 100 includesan example automatic image recognition application 112 that analyzes thecaptured image 104 in attempt to identify the product(s) 108 a-108 n inthe captured image 104. The automatic image recognition application maybe implemented by an application, software, engine, algorithm, hardware,etc. or any combination thereof. The automatic image recognitionapplication 112 compares the captured image 104 to reference productimages (e.g., records) stored in a product database 114 to attempts tomatch the product(s) 108 a-108 n in the capture image 104 to catalogued(e.g., known) products. The reference product images are images of knownproducts. In some examples, the reference product images are provided byan external source 116 (e.g., a manufacturer, a reference imagecollection team, the Internet, etc.). Example techniques that may beused to implement the automatic image recognition application 112include, but are not limited to, examples described in: U.S. patentapplication Ser. No. 14/530,129, entitled “Context-Based ImageRecognition For Consumer Market Research,” and filed Oct. 31, 2014,which corresponds to U.S. Patent Publication Ser. No. ______, whichpublished on ______; U.S. Pat. No. 8,908,903, entitled “ImageRecognition to Support Shelf Auditing for Consumer Research,” andgranted on Dec. 9, 2014; and U.S. Pat. No. 8,917,902, entitled “ImageOverlaying and Comparison for Inventory Display Auditing,” and grantedon Dec. 23, 2014, all of which are incorporated herein by reference intheir entireties.

In the illustrated example, the example auditing system 100 includes animage segmentor 118, which segments the captured image 104 analyzed bythe automatic image recognition application 112. In particular, theimage segmentor 118 generates a frame (e.g., a rectangle, a grid, asquare, an object, etc.) around the one or more productsrecognized/identified in the captured image 104 by the automatic imagerecognition application 112. In some examples, the example imagesegmentor 118 separates the captured image 104 into one or more images(e.g., sub-images) that each depict a respective one of the product(s)108 a-108 n. In some examples, the captured image is segmented, via theimage segmentor 118 first, and then the segmented image is analyzed bythe automatic image recognition application 112 in attempts to match thesegments to the reference image records in the product database 114. Insome examples, the image segmentor 118 performs segmentation by using apattern recognition algorithm to compare particular product referenceproduct images (e.g., selected based on stockkeeping units (SKUs) and/orother product identification stored in the product records in theproduct record database 114) with the captured image 104 to identify theboundaries of the products 108 a-108 n. Additionally or alternatively,the captured image 104 may be segmented through user input, as disclosedin further detail herein. Although illustrated as separate, in someexamples the image segmentor 118 is implemented as part of the automaticimage recognition application 112. For example, the image analysis andsegmentation process may occur simultaneously via the same applicationor program. In the illustrated example, the automatic image recognitionapplication 112 and image segmentor 118 may be located within theexample auditing system 100. However, in other examples the automaticimage recognition application 112 and/or the image segmentor 118 may belocated offsite, and the results of which may then communicated to theauditing system 100. For example, the automatic image recognitionapplication 112 and/or the image segmentor 118 may be supplied by athird party.

As disclosed herein, the example automatic image recognition application112 and/or the image segmentor 118 attempt to recognize/identify andmatch the product(s) 108 a-108 n in the captured image 104 with thereference image records stored in the product database 114. Examplereference image records include, for example, reference imagesrepresenting respective reference products, information describing therespective reference products, etc. Further example records aredisclosed in U.S. patent application Ser. No. 14/530,129, entitled“Context-Based Image Recognition For Consumer Market Research,” andfiled Oct. 31, 2014, which corresponds to U.S. Patent Publication Ser.No. ______, which published on ______, and which is incorporated hereinby reference in its entirety.

The automatic image recognition application 112 and/or the imagesegmentor 118 create or generate a segmented image (e.g., such as thesegmented image 200 of FIG. 2, discussed in further detail herein) thatmay be stored in a match database 120 and which may be viewed on anexample user interface 122. The user interface 122 may be implemented byany type of imaging display using any type of display technology, suchas a liquid crystal display (LCD), a light emitting diode (LED) display,a plasma display, a cathode ray tube (CRT) display, etc., one or moreinput devices (e.g., a touchscreen, touchpad, keypad, mouse, keyboard,etc.) and/or one or more of the output devices included in the exampleprocessing system 900 of FIG. 9, which is described in greater detailbelow. Once the automatic image recognition application 112 and/or theimage segmentor 118 generates the segmented image, the segmented imagemay be displayed on the user interface 122 for an auditor to review.

FIG. 2A illustrates an example segmented image 200 corresponding to thecaptured image 104 produced by the automatic image recognitionapplication 112 and/or the image segmentor 118. The segmented image 200may also be referred to as a segmentation 200. The segmented image 200includes one or more frames 202 a-202 n (e.g., an object, a boundary, arectangle, a grid, etc.) overlaid on top of the captured image 104. Theframes 202 a-202 n indicate the locations (e.g., positions, boundaries)of the products 108 a-108 n in the captured image 104 that have beendetected and identified by the automatic image recognition application112 and/or the image segmentor 118. In the illustrated example, theframes 202 a-202 n include identifying information (e.g., a name of therespective products 108 a-108 n) along a top of the frames 202 a-202 n.The identifying information included in respective ones of the frames202 a-202 n is associated with the reference product(s) determined tomatch the corresponding products 108 a-108 n depicted in the frames 202a-202 n and is stored in the match database 120. In particular, if amatch is found for one of the products 108 a-108 n, the automatic imagerecognition application 112 and/or the image segmentor 118 stores amatch record in the match database 120 for the specific product 108a-108 n. In some examples, the match record includes the captured image104, metadata associated with the captured image (if present), and/or aproduct identifier as returned from the reference record associated withthe matching reference image (e.g., an MRE assigned product number, aUPC, etc.). Each of the match records is associated with a particularone of the frames 202 a-202 n.

In some examples, as illustrated in FIG. 1, the information included inthe match record(s) is used by an example report generator 124 togenerate an example match report 126. In some examples, the matchrecord(s) are stored in the match database 120 and/or included in thematch report 126 and/or are used by the example auditing system 100 togenerate shelf-audit reports.

However, as mentioned above, in some automatic image recognitionapplications, the created segmented images, while effective in mostsituations, are reviewed by an auditor (e.g., a user, an operator, etc.)to verify the accuracy of the results (i.e., the products identified asmatching the frames 202 a-202 n). In some instances, the automatic imagerecognition application 112 and/or the image segmentor 118 does notdetect the location of a product and/or cannot find a match for thesegmented product. As such, auditors may spend numerous hours reviewingthe results of the automatic image recognition process and attempting tomanually identify the product(s) shown in the captured image 104. Inparticular, this process may be separated into two tasks: (1)identifying one or more regions of interest (ROIs), which are areaswhere products that still need to be segmented appear, and (2) labelingthe products in the ROI(s). Labeling the products may involve manuallysearching through a database containing thousands of products and thenassigning an item number, such as an SKU, and/or International ArticleNumber (EAN) code to the product depicted in an ROI once a referenceproduct is located. This process of manually reviewing thousands ofproducts in a database and assigning SKU/EAN codes may requiresubstantial time and experience on behalf of the auditor.

For example, FIG. 2B illustrates an example portion 206 of the segmentedimage 200. In the illustrated example of FIG. 2B, the cans on the leftside of the portion 206 were not detected by the automatic imagerecognition application 112 and/or the image segmentor 118. As a result,the cans on the left side of the portion 206 were not were not segmentedor framed and, thus, were not matched with any products. This may occurfor a number of reasons such as, but not limited to, improperly facingproducts, lighting, obstructions, etc. Additionally, in some instances,the automatic image recognition application 112 may incorrectly label aproduct. For example, as shown FIG. 2B, the Amstel® cans on the righthave been incorrectly labeled as Heineken®. Again, this may occur of anumber of reasons, including improperly facing products, lighting,obstructions, etc.

The example auditing system 100 of FIG. 1 enables an auditor to identifyan ROI in the captured image 104, and then performs a confidence searchto return one or more candidate products that may be in the ROIidentified by the auditor. The candidate products are assignedrespective confidence levels that indicate degrees of certainty orlikelihoods that the respective candidate products are the same as theproduct in the ROI. To identify an ROI, for example, an auditor mayhighlight (e.g., using a mouse or other input device) an area on thesegmented image 200. For example, FIGS. 2C and 2D illustrate an auditorusing an example cursor 208 to highlight an ROI 210 containing theproduct 108 g. In the illustrated example, the ROI 210 is an area in thesegmented image 200 showing the product 108 g, which, in the illustratedexample, was not segmented or identified by the example automatic imagerecognition application 112 and/or the example image segmentor 118. Oncethe ROI 210 is highlighted, an example frame 202 g (e.g., a boundary, agrid, a rectangle, a square, an object, etc.) is generated around theROI 210 (e.g., which corresponds to the cans on the left in FIG. 2B thatwere not segmented), as illustrated in FIG. 2D.

To detect or identify the product(s) 108 g depicted in the ROI 210identified by the auditor (e.g., based on user input), the exampleauditing system 100 of FIG. 1 includes an example ROI detector 128,which detects the ROI 210 (e.g., a sub-image) of the captured image 104.Once the auditor defines the ROI 210 and the ROI detector 128 detectsthe ROI 210, an example candidate product searcher 130, included in theexample auditing system 100 of FIG. 1, automatically analyzes the ROI210 to identify one or more candidate products that may potentiallymatch the product 108 g in the ROI 210. The candidate product searcher130 compares the ROI 210 to one or more reference product records storedin the product database 114. In some examples, this process occurs whilethe auditor continues to define other ROIs in the captured image 104.For example, the captured image 104 may contain multiple areas whereproducts were not detected by the automatic image recognitionapplication 112 and/or the image segmentor 118 and, thus, were notsegmented. The auditor may create frames (e.g., similar to the frame 202g) around each of the ROIs. In some examples, while the auditor iscreating the frames, the example auditing system 100 processes theidentified ROI(s) for prior identified frames to identify one or morecandidate products, as disclosed in further detail herein.

For example, when the example candidate product searcher 130 beginsanalyzing the ROI 210, the example frame 202 g may indicate the productis undefined, as depicted in the example of FIG. 2D. In some examples,an icon (e.g., a magnifying glass, an hourglass, etc.) is shown over theROI 210 to indicate the candidate product searcher 130 is searching forcandidate products. An example implementation of the candidate productsearcher 130 is illustrated in FIG. 5, which is described in furtherdetail herein. Once the search is complete, a product name (e.g.,“Cruzcampo”) and an estimation of the layout (indicated by the number ofrows and columns) may be displayed in the frame 202 g over the ROI 210,as illustrated in the example of FIG. 2E. In some examples, the name ofthe product corresponds to the candidate product with the highestconfidence level, as disclosed in further detail herein.

To review the candidate products and assist an auditor in verifyingand/or correcting the product matched with the product in the ROI 210,the example auditing system 100 of FIG. 1 includes an example quickpreview window generator 132 (e.g., a preview window generator) thatgenerates an example quick preview window 300 (e.g., a preview window),as shown in FIG. 3. The quick preview window 300 may be implemented asany graphical user interface, window, panel, etc. displayable on theuser interface 122. In some examples, the quick preview window 300 isdisplayed, via the user interface 122, simultaneously with the ROI 210.For example, the quick preview window 300 may be displayed over asection or portion of the segmented image 200 adjacent (e.g., near) theROI 210. In other examples, the quick preview window 300 may bedisplayed adjacent the segmented image 200 (e.g., next to the segmentedimage 200 on the user interface 122).

In the illustrated example, the quick preview window 300 includes acandidate section 302 that has a list of one or more candidate products304 a-304 n identified by the candidate product searcher 130 aspotential candidates for the product 108 g. In some examples, thecandidate products 304 a-304 n are assigned respective confidence levels(e.g., a score, a value).

In the illustrated example, the candidate products 304 a-304 n arelisted in order of their respective confidence levels (e.g., fromhighest to lowest). In some examples, the confidence levels aredisplayed adjacent the respective candidate products 304 a-304 n (e.g.,“90%” may be displayed next to the candidate product 304 a). Theconfidence levels represent degrees of certainty or likelihoods that therespective candidate products 304 a-304 n match the product 108 g. Insome examples, the quick preview window 300 is automatically displayed(e.g., opened, presented, etc.) on the user interface 122 once thecandidate product searcher 130 is finished searching the records in theproduct database 114. In other examples, the quick preview window 300may be displayed based on user input such as, for example, when the userselects the frame 202 g. For example, after creating the frame 202 g,the auditor may continue to identify other ROIs in the captured image104. While the auditor is creating the other frames, the candidateproduct searcher 130 performs the confidence search and obtains thecandidate products 304 a-304 n for the frame 202 g. Then, when theauditor desires, the auditor may select (e.g., by clicking) a previouslycreated frame (e.g., the frame 202 g) to open the quick preview window300 for the respective ROI (e.g., the region of the image associatedwith or bounded by the frame).

In some examples, the candidate product having the highest confidencelevel is automatically assigned by the candidate product searcher 130 tothe product 108 g in the ROI 210. In some examples, the candidateproduct searcher 130 creates and/or updates a record (e.g., in the matchdatabase 120) of the frame 202 g and/or the product 108 g to indicatethe product 108 g is matched to the candidate product. In theillustrated example of FIG. 3, the quick preview window generator 132also generates the example quick preview window 300 such that each ofthe candidate products 304 a-304 n includes a respective reference image(e.g., a thumbnail) of the respective reference product and the name ofthe respective reference product. In the illustrated example, a firstcandidate product 304 a is highlighted, indicating the first candidateproduct 304 a is currently assigned or matched to the product 108 g inthe ROI 210 (e.g., by the candidate product searcher 130). If the firstcandidate product 304 a is incorrect, the auditor may select (e.g., byclicking) another one of the candidate products 304 b-304 n in thecandidate section 302 to assign the respective candidate product 304b-304 n to the product 108 g in the ROI 210.

In the illustrated example, the product information related to thecurrently assigned product is displayed in a product information section306 of the quick preview window 300. The product information section 306includes product information assigned (e.g., stored as a match record inthe match database 120) to the product 108 g in the ROI 210. Forexample, the product information section 306 may include a referenceproduct image 308 (e.g., a thumbnail) corresponding to the respectivereference product determined to match the product 108 g, a type or name310 of the matching reference product, a view 312 indicating the view ororientation (e.g., frontal, side, top, etc.) of the product 108 g (e.g.,relative to the matching reference product's image), facings information314 indicating a number of facings of the product identified in the ROI210 and the corresponding layout in number of rows 316 and columns 318of the facings that appear in the ROI 210, stock information 320 (e.g.,which may be a button, an indicator, an icon, a flag, etc.) to indicatewhether the product is out of stock or not (e.g., based on informationassociated with the matching reference product in the product database114), pallet information 322 indicating if the product is on a pallet ornot (e.g., based on information associated with the matching referenceproduct in the product database 114), metric information 324 (e.g.,based on information associated with the matching reference product inthe product database 114), price information 326 and a range 328 of theprice (e.g., based on information associated with the matching referenceproduct in the product database 114), etc. In other examples, theproduct information section may display more or less productinformation.

The example quick preview window 300 enables an auditor to quickly andeasily determine and/or modify the information assigned (e.g., stored asa record in the match database 120) to the product 108 g of the ROI 210.If the information is changed, the quick preview window generator 132updates the record for the respective frame (e.g., the frame 202 g) orproduct (e.g., the product 108 g) based on the modification. Forexample, if the auditor views the candidate section 302 and determines asecond product 304 b is the correct product, the auditor may select thesecond product 304 b in the quick preview window 300 to assign thesecond candidate product 304 b to the product of the ROI 210. In anotherexample, the auditor can set or change the view 312 of the product inthe ROI 210. In another example, the auditor can establish if theproduct 108 g is out of stock. For example, the auditor may compare aposition of the product 108 g in the shelf 106 in the image 104 with thesame or similar position in the same shelf 106 from a previous image, ifavailable. If the product is not the same, the auditor can establish(e.g., by clicking or selecting a flag or button) the product is out ofstock.

In some examples, the metric information 324 is provided to notify theauditor of any potential errors in the product information. For example,the auditing system 100 may compare the view 312 option chosen by theauditor against a probability that the product 108 g belongs to thespecific view. In some examples, the probability is obtained byprocessing data previously obtained (e.g., from other point-of-saleimage audits). If the auditing system 100 determines the view 312 optionchosen by the auditor is not probable, the metric information 324 mayinclude an indication (e.g., an instruction, a warning, etc.) that sucha view is not probable. In another example, the auditing system 100 maydetermine if there are any potential errors in the number of the rows316 and the columns 318 based on the view 310, the number of facings314, the recognized product, and/or the pixel to millimeter (pixel/mm)ratio of the image of the product 108 g. If the auditing system 100identifies a potential error in the number of the rows 316 and thecolumns 318, the metric information 324 may include an indication (e.g.,an instruction, a warning, etc.) that such numbers are potentiallyincorrect.

Additionally or alternatively, in some examples, the auditing system 100may determine if there any potential errors in the pallet information322 by comparing the option as chosen by the auditor against aprobability that the product 108 g belongs to a pallet. In someexamples, the probability is obtained by processing data previouslyobtained (e.g., from other point-of-sale image audits). If the auditingsystem 100 determines the pallet information 322 is potentiallyincorrect, the metric information 324 may include an indication (e.g.,an instruction, a warning, etc.) that such a selection is potentiallyincorrect.

Additionally or alternatively, in some examples, the auditing system 100may compare the price 326 chosen by the auditor against previous pricesof the same product (e.g., in the same store). If the auditing system100 determines the price 326 is potentially incorrect, the metricinformation 324 may include an indication (e.g., an instruction, awarning, etc.) that such a price is not probable.

In the illustrated example of FIG. 3, the quick preview window 300includes a search bar 330. An auditor may type a word (e.g., a producttype, a name of a product, a brand, etc.) into the search bar 330 toretrieve or obtain additional reference products from the productdatabase 114. The retrieved product may be displayed in the candidatesection 302, for example.

In some examples, the quick preview window generator 132 may generate orcreate the quick preview window 300 for one or more of the products 108a-108 n already identified by the automatic image recognitionapplication 112 and/or the image segmentor 108 (without including ROIdetection, candidate product searching, etc.). As such, the productinformation related to the recognized product may be displayed via theuser interface 122. For example, FIG. 4 illustrates an example segmentedimage 400 showing a product 402 detected during the automatic imagerecognition process. The example segmented image 400 includes a frame404 having a name (e.g., “Mahou”) of the recognized product disposedover the product 402. The auditor may select (e.g., by clicking) on theframe 404 to view the product information related to the product 402.Once the auditor selects the frame 404, the quick preview window 300 isdisplayed adjacent the frame 404 (e.g., adjacent the product 402).Similar to above, an auditor may set and/or modify the productinformation in the product information section 306 of the quick previewwindow 300. In the illustrated example, the product information includesthe reference image 308 of the recognized product, the name 310 of therecognized product, the view 312, the facings information 314, the stockinformation 320, the pallet information 322, the metric information 324and the price information 326 (e.g., which may include a range of theprice). In other examples, more or less product information may bedisplayed.

In some examples, when the frame 404 is selected by the auditor, thecandidate product searcher 130 operates, as disclosed herein, toidentify one or more candidate products 406 a-406 n potentiallycorresponding to the product 402 depicted in the frame 404. In some suchexamples, the candidate product(s) 406 a-406 n are displayed in thequick preview window 300 from which the auditor may select to change thereference product assigned (e.g., matched) to the product 402. In theillustrated example, the candidate products 406 a-406 n are listed inthe candidate section 302 of the quick preview window 300. In someexamples, the candidate products 406 a-406 n are obtained via aconfidence search performed by the example candidate product searcher130 of FIG. 1, as noted above. However, in other examples, the candidateproducts 406 a-406 n may be based on reference products identifiedduring the automatic image recognition process (e.g., performed by theautomatic image recognition application 112) as potentially matching theproduct 402. In some examples, the candidate products 406 a-406 n areobtained using a string comparison algorithm. In some examples, thecandidate products 406 a-406 n are assigned respective confidence levelsand the candidate products 406 a-406 n may be sorted by the confidencelevels. If an auditor desires to change the product recognized in theframe 404, the auditor may select one of the candidate products 406a-406 n, which then updates the product information in the productinformation section 306. In the illustrated example, the quick previewwindow 300 includes the search bar 330, which enables an auditor tofurther search for products in the product database 114.

An example implementation of the candidate product searcher 130 includedin the example auditing system 100 of FIG. 1 is illustrated in FIG. 5.In the illustrated example, the candidate product searcher 130 includesan example image corner detector 500, an example feature extractor 502,an example feature relation identifier 504, an example dominanttransformation projection identifier 506, an example layout estimator508 and an example confidence level determiner 510.

Once the ROI 210 is identified or detected by the ROI detector 128, theimage corner detector 500 detects the corners of the ROI 210 from thepixels in the ROI 210. The corners of an image, for example, may containmeaningful pixels that can be used to identify the product contained inthe ROI 210. For example, the corner detector 500 may determine whethera pixel is a corner or not based on comparing information (e.g.,location, color, etc.) for pixels in a neighborhood to identifycharacteristics consistent with a corner (e.g., two intersecting edgesterminating at a given location). In some examples, the image cornerdetector 500 uses an optimal accelerated segment test (OAST) to detectthe corners of the ROI image 210. A description the OAST is found inElmar Mair et al., “Adaptive and Generic Corner Detection Based on theAccelerated Segment Test,” Proceedings of the European Conference onComputer Vision (ECCV'10), September 2010, hereby incorporated byreference in its entirety. However, in other examples, other imagecorner detection techniques may be employed.

In the illustrated example, the feature extractor 502 identifies and/ordescribes features (e.g., key points or characteristics of an imagecontaining relevant information). In some examples, the features areassociated with the corners of the ROI 210 detected by the image cornerdetector 500. In some examples, the features are identified using theOAST disclosed above, and the feature extractor 502 describes thefeatures using windows of pixels. For example, the features may include5×5 windows of normalized raw pixel values in grayscale (e.g., by takingthe values of the pixels and subtracting the mean and dividing by thestandard deviation (SD), yielding a normal distribution (mean 0, SD 1)for the normalized pixels). In other examples, other size windows may beused. The example image corner detector 500 and the example featureextractor 502 are to perform the same functions on reference productimage(s) depicting reference product(s) stored in the product database114, for example, to extract (e.g., identify, determine) and describefeatures and/or corners of the reference product image(s).

In the illustrated example, the example feature relation identifier 504compares the features extracted from the ROI 210 with the featuresextracted from a reference product image corresponding to a referencerecord selected from the product database 114 to determine or identifyany features that are similar (e.g., match, form a relationship, etc.).The example feature relation identifier 504 identifies relationships(e.g., relations), among features of the ROI 214 and of the referenceimage that are similar. In some examples, however, there may be arelatively large amount of features extracted from the ROI 210 and/orthe reference image. As a result, this relation identification processmay take significant time (e.g., when comparing 5000 features of oneimage to 30,000 features of another image). In some examples, one ormore relationships among one group of features may not be compatiblewith the relationships among other groups of features. For example, therelationships between similar features among the ROI 210 and thereference image may be vectors defining the relative difference inlocation of the features in the different images. In such an example,two features having a relationship characterized by a large angle wouldnot be compatible without a group of relationships for a group offeature pairs each having relatively small angles. To decreaseprocessing time needed to compare the ROI 210 and the reference image,in some examples a metric is applied that determines whether therelationship is compatible or not. In other words, the metric can beapplied to discriminate which relationships have a high grade ofcorrelation (e.g. a high degree of similarity). For example, therelationships can be assigned grades of correlation (e.g., a score orvalue of similarity), which can be compared to a threshold to determineif the relationship is compatible or not. In some examples, the metricis the Pearson product-moment correlation coefficient (PPMCC or PCC).

Once matching features are found and/or the relationships areidentified, the example dominant transformation projection identifier506 identifies a dominant transformation projection (e.g., amathematical formula or function) that transforms the features from oneof the images (e.g., the ROI 210) to the other image (e.g., thereference image) and which can translate a point from the ROT image 214to the reference image, or vice versa. In some examples, a random sampleconsensus (RANSAC) algorithm is used. In some examples, the dominanttransformation projection represents the most important or influentialmatch or relationship between the features of the ROI 210 and thereference image. In some examples, the dominant transformationprojection identifier 506 identifies the area (e.g., a rectangle) of theproduct, from which the position of the product (e.g., front facing,side facing, top facing, etc.) is estimated.

In some examples, the example dominant transformation projectionidentifier 506 produces a scale of the reference image. Using thisscale, the example layout estimator 508 determines the layout of theproduct in the ROI 210, which may include the number of rows and columnsof the product in the ROI 210. For example, as illustrated in FIG. 3,the number of rows of the product is 2 and the number of columns is 4.

The example confidence level determiner 510 calculates a confidencelevel (e.g., a value, a score) based on the number of inliers, which isthe number of features that are compatible with the dominanttransformation projection (e.g., that lie within an area of theproduct), as determined by the dominant transformation projectionidentifier 506. In some examples, the more inliers that are identifiedthe higher the confidence level. The example candidate product searcher130 may repeat this process for any number of reference images (e.g.,patterns) stored in the product database 114. After processing thereference images (e.g., comparing the features extract from the ROI 210to features extracted from the reference images), the example candidateproduct searcher 130 sorts the candidate products (associated with therespective reference images) based on the confidence levels, which maythen be presented to the auditor via the example quick preview window300 (FIG. 3). In some examples, the confidence levels of the referenceimages are compared to a threshold, and only candidate products havingreference images that satisfy the threshold are displayed by the examplequick preview window 300.

While example manners of implementing the auditing system 100 areillustrated in FIGS. 1-5, one or more of the elements, processes and/ordevices illustrated in FIGS. 1-5 may be combined, divided, re-arranged,omitted, eliminated and/or implemented in any other way. Further, theexample automatic image recognition application 112, the example imagesegmentor 118, the example report generator 124, the example ROIdetector 128, the example candidate product searcher 130, the examplequick preview window generator 132, the example image corner detector500, the example feature extractor 502, the example feature relationidentifier 504, the example dominant transformation projectionidentifier 506, the example layout estimator 508, the example confidencelevel determiner 510 and/or, more generally, the example auditing system100 of FIGS. 1-5 may be implemented by hardware, software, firmwareand/or any combination of hardware, software and/or firmware. Thus, forexample, any of the example automatic image recognition application 112,the example image segmentor 118, the example report generator 124, theexample ROI detector 128, the example candidate product searcher 130,the example quick preview window generator 132, the example image cornerdetector 500, the example feature extractor 502, the example featurerelation identifier 504, the example dominant transformation projectionidentifier 506, the example layout estimator 508, the example confidencelevel determiner 510 and/or, more generally, the example auditing system100 could be implemented by one or more analog or digital circuit(s),logic circuits, programmable processor(s), application specificintegrated circuit(s) (ASIC(s)), programmable logic device(s) (PLD(s))and/or field programmable logic device(s) (FPLD(s)). When reading any ofthe apparatus or system claims of this patent to cover a purely softwareand/or firmware implementation, at least one of the example, automaticimage recognition application 112, the example image segmentor 118, theexample report generator 124, the example ROI detector 128, the examplecandidate product searcher 130, the example quick preview windowgenerator 132, the example image corner detector 500, the examplefeature extractor 502, the example feature relation identifier 504, theexample dominant transformation projection identifier 506, the examplelayout estimator 508, and/or the example confidence level determiner 510is/are hereby expressly defined to include a tangible computer readablestorage device or storage disk such as a memory, a digital versatiledisk (DVD), a compact disk (CD), a Blu-ray disk, etc. storing thesoftware and/or firmware. Further still, the example auditing system 100may include one or more elements, processes and/or devices in additionto, or instead of, those illustrated in FIGS. 1-5, and/or may includemore than one of any or all of the illustrated elements, processes anddevices.

Flowcharts representative of example machine readable instructions forimplementing the example automatic image recognition application 112,the example image segmentor 118, the example report generator 124, theexample ROI detector 128, the example candidate product searcher 130,the example quick preview window generator 132, the example image cornerdetector 500, the example feature extractor 502, the example featurerelation identifier 504, the example dominant transformation projectionidentifier 506, the example layout estimator 508, the example confidencelevel determiner 510 and/or, more generally, the example auditing system100 of FIG. 1-5 are shown in FIGS. 6, 7 and 8. In this example, themachine readable instructions comprise a program for execution by aprocessor such as the processor 912 and/or the processor 1012 shown inthe example processor platforms 900, 1000 discussed below in connectionwith FIGS. 9 and 10. The program may be embodied in software stored on atangible computer readable storage medium such as a CD-ROM, a floppydisk, a hard drive, a digital versatile disk (DVD), a Blu-ray disk, or amemory associated with the processor 912 and/or the processor 1012, butthe entire program and/or parts thereof could alternatively be executedby a device other than the processor 912 or the processor 1012 and/orembodied in firmware or dedicated hardware. Further, although theexample program is described with reference to the flowchartsillustrated in FIGS. 6, 7 and 8, many other methods of implementing theexample automatic image recognition application 112, the example imagesegmentor 118, the example report generator 124, the example ROIdetector 128, the example candidate product searcher 130, the examplequick preview window generator 132, the example image corner detector500, the example feature extractor 502, the example feature relationidentifier 504, the example dominant transformation projectionidentifier 506, the example layout estimator 508, the example confidencelevel determiner 510 and/or, more generally, the example auditing system100 may alternatively be used. For example, the order of execution ofthe blocks may be changed, and/or some of the blocks described may bechanged, eliminated, or combined.

As mentioned above, the example processes of FIGS. 6, 7 and 8 may beimplemented using coded instructions (e.g., computer and/or machinereadable instructions) stored on a tangible computer readable storagemedium such as a hard disk drive, a flash memory, a read-only memory(ROM), a compact disk (CD), a digital versatile disk (DVD), a cache, arandom-access memory (RAM) and/or any other storage device or storagedisk in which information is stored for any duration (e.g., for extendedtime periods, permanently, for brief instances, for temporarilybuffering, and/or for caching of the information). As used herein, theterm tangible computer readable storage medium is expressly defined toinclude any type of computer readable storage device and/or storage diskand to exclude propagating signals and to exclude transmission media. Asused herein, “tangible computer readable storage medium” and “tangiblemachine readable storage medium” are used interchangeably. Additionallyor alternatively, the example processes of FIGS. 6, 7 and 8 may beimplemented using coded instructions (e.g., computer and/or machinereadable instructions) stored on a non-transitory computer and/ormachine readable medium such as a hard disk drive, a flash memory, aread-only memory, a compact disk, a digital versatile disk, a cache, arandom-access memory and/or any other storage device or storage disk inwhich information is stored for any duration (e.g., for extended timeperiods, permanently, for brief instances, for temporarily buffering,and/or for caching of the information). As used herein, the termnon-transitory computer readable medium is expressly defined to includeany type of computer readable storage device and/or storage disk and toexclude propagating signals and to exclude transmission media. As usedherein, when the phrase “at least” is used as the transition term in apreamble of a claim, it is open-ended in the same manner as the term“comprising” is open ended.

FIG. 6 is a flowchart representative of an example method 600 forauditing a point-of-sale image, which may be implemented at least inpart by machine readable instructions executed by the example auditingsystem of FIG. 1. The example method 600 corresponds to a workflow forperforming a product audit of the image 104, for example. At block 602of the example method 600, the automatic image recognition application112 analyzes the captured image 104 and attempts to identify theproducts 108 a-108 n in the captured imaged 104. The image segmentor118, at block 604, segments or divides the captured image 104 into oneor more frames 202 a-202 n to create the segmented image 200.

At block 606, the example method 600 includes determining whether thereis an unrecognized product in the segmented image 200. For example, anauditor may view the segmented image 200 on the user interface 122. If,for example, the auditor determines that all of the products 108 a-108 nin the segmented image 200 have been recognized (block 606), the method600 ends. However, as mentioned above, in some instances the automaticimage recognition application 112 and/or the image segmentor 118 doesnot recognize all of the products 108 a-108 n in the captured image 104.When reviewing the segmented image 200, the auditor may see that one ofthe products (e.g., the product 108 g of FIG. 2C) in the segmented image200 has not been recognized.

If there is an unrecognized product (block 606), the example method 600at block 608 includes identifying the ROI 210. For example, asillustrated in FIGS. 2C and 2D, the auditor may create the frame 202 g(e.g., by drawing a boundary) around the ROI 210. In such examples, atblock 608 the ROI detector 128 detects the ROI 210 identified by theauditor for which the candidate product searcher 130 searches forcandidate products that potentially match a product depicted in the ROI210, as disclosed in further detail herein. For example, at block 610,the candidate product searcher 130 performs a confidence search toidentify one or more candidate products that may appear in the ROI 210.An example process for performing a confidence search to identifycandidate products is illustrated in the example flowchart of FIG. 7 anddiscussed in further detail herein.

At block 612, the candidate product searcher 130 assigns one of thecandidate products 304 a-304 n to the product(s) in the ROI 210. Forexample, as illustrated in FIG. 3, the first candidate product 304 a isautomatically matched or assigned by the candidate product searcher 130as identifying the product in the ROI 210 (e.g., because it isassociated with the highest confidence level). In other examples, otherones of the candidate products 304 a-304 n may be assigned.

At block 614, the example method includes determining whether theproduct depicted in the ROI 210 is identified correctly. The auditor mayview the name of the recognized (e.g., assigned, matched) product in theframe 202 g depicted over the ROI 210. If the product is identifiedcorrectly, the example method 600 includes again determining whetherthere is another unrecognized product in the segmented image 200.Otherwise, if the product is not identified correctly, the examplecandidate products 304 a-304 n may be displayed or presented to theauditor at block 616. For example, the quick preview window generator132 generates the quick preview window 300 to be displayedsimultaneously as the ROI 210 (e.g., over a section of the segmentedimage 200 adjacent the ROI 210). The quick preview window 300 includesthe candidate section 302, which displays the candidate products 304a-304 n returned from the confidence search. In some examples, the quickpreview window 300 is displayed when an auditor selects the frame 202 g.In other examples, the quick preview window 300 is automaticallydisplayed after the confidence search is performed.

At block 618, the example method 600 includes selecting one of thecandidate products 304 a-304 n as matching the product 108 g in the ROI210. For example, referring to FIG. 3, an auditor may select (e.g., viaa mouse click) the second candidate product 304 b in the candidatesection 302 to assign to the second candidate product 304 b to the ROI210 (e.g., to the product depicted in the ROI 210). At block 620, thematch record stored in the match database 120 is updated to reflect thechange in the reference product assigned to the ROI 210. The examplemethod 600 again includes determining whether there is anotherunrecognized product at block 606. If there is another unrecognizedproduct, the example method 600 continues. This cycle may be repeatedany number of times until all of the products in the segmented image 200have been recognized and/or are verified as correct. Otherwise, theexample method 600 ends.

FIG. 7 is a flowchart representative of an example method 700 to performa confidence search, which may correspond to the processing performed atblock 610 of FIG. 6. The example method 700 of FIG. 7 includes machinereadable instructions that may be executed to implement and/or executedby the example candidate product searcher 130 of FIGS. 1 and 5 and/or,more generally, the example auditing system 100 of FIG. 1, for example.

At block 702 of FIG. 7, the example image corner detector 500 detectsthe corners of the ROI 210. The corners of an image may containmeaningful pixels, for example. In some examples, the image cornerdetector 500 uses an OAST to detect the corners of the ROI 210. However,in other examples, other image corner detection techniques may beemployed. At block 704 of the example method 700, the example featureextractor 502 extracts and/or describes the features (e.g., which may beincluded in the corners) and/or the corners detected by the image cornerdetector 500 (e.g., at block 702). In some examples, each featurecorresponds to a 5×5 window of normalized row pixel values in grayscale.

At block 706, the example image corner detector 500 detects corners of areference image (e.g., a pattern) stored in the product database 114.The image corner detection may be performed similar to block 702. Insome examples, the image corner detector 500 uses the OAST to detect thecorners of the reference image. At block 708, the example featureextractor 502 extracts and/or describes features and/or the cornersdetected by the image corner detector 500. The feature extraction may beperformed similar to block 704.

At block 710, the example feature relation identifier 504 compares thefeatures extracted from the ROI 210 to the features extracted from thereference image to determine or identify any features that are similar(e.g., match). Features that are similar are identified andcharacterized by a relationship. In some examples, not all relationshipare compatible. Therefore, a metric, such as the Person product-momentcorrelation coefficient, may be applied to determine if the relationshipis compatible or not. At block 712 of FIG. 7, the example dominanttransformation projection identifier 506 identifies the dominanttransformation projection, which includes calculating or determining amathematical formula or function that transforms the features betweenthe ROI 210 and the reference image. In other words, the dominanttransformation projection can translate a point from the ROI 210 to thereference image, or vice versa. In some examples, an RANSAC algorithm isused. In some examples, the example dominant transformation projectionidentifier 506 identifies the most important or influential match orrelationship.

At block 714 in FIG. 7, the layout estimator 508 determines or estimatesthe layout (e.g., configuration, arrangement) of the reference productin the ROI 210. In some examples, the dominant transformation projectionis a scale of the reference image, which is then used by the examplelayout estimator 508 to determine the layout of the reference product(e.g., in rows and columns), in the ROI 210.

At block 716, the confidence level determiner 510 identifies the numberof features (e.g., inliers) that are compatible within the dominanttransformation projection. Based on the number of inliers, theconfidence level determiner 510 determines (e.g., measures) theconfidence level of the candidate product image associated with thereference product image (e.g., the reference product). At block 718, theexample method 700 includes determining whether there is anotherreference image (e.g., a pattern) to analyze. In the illustrated exampleof FIG. 7, the process of comparing the extracted features of the ROIimage 210 to a reference image may be repeated for another referenceimage, and so on (block 718). Once the reference images are analyzed,the example candidate product searcher 130 sorts the candidate products304 a-304 n by their respective confidence levels (block 720), which maythen be presented to the auditor via the quick preview window 300, asdisclosed herein. The candidate products 304 a-304 n may be displayed inorder of highest confidence level to lowest confidence level, or viceversa. In some examples, the confidence levels are displayed adjacentthe candidate products 304 a-304 n.

FIG. 8 is flowchart representative of an example method 800 fordisplaying product information associated with a detected product andupdating a record of the respective product, which may be implemented atleast in part by machine readable instructions executed by the exampleauditing system of FIG. 1. The example method 800 may correspond to aworkflow for performing a product audit of the segmented image 200, forexample. In general, the example method 800 is described in connectionwith the segmented image 200 illustrated in FIG. 2. However, the examplemethod may similarly be performed on the segmented image 400 illustratedin FIG. 4.

At block 802 of FIG. 8, the example automatic image recognitionapplication 112 analyzes the captured image 104 and attempts to identifythe products 108 a-108 n in the captured imaged 104. The image segmentor118, at block 804, segments or divides the captured image 104 into theone or more frames 202 a-202 n to create the segmented image 200. Insome examples, as disclosed herein, the segmented image 200 may befurther segmented based on user input. For example, an auditor mayidentify an ROI in the segmented image 200, and a frame may be displayedaround the ROI, as described in connection with the methods 600 and 700of FIGS. 6 and 7.

At block 806, the segmented image 200 is displayed via the userinterface 122. At block 808, the example quick preview window generator132 renders the quick preview window 300 to display product informationsimultaneously with the product 108 g of the ROI 210. In some examples,the quick preview window 300 is displayed in response to user input(e.g., the auditor selects the frame 202 g and/or the ROI 210). Asillustrated in FIG. 3, the quick preview window 300 displays productinformation associated with the recognized product. The quick previewwindow 300 may be displayed adjacent (e.g., near) the selected ROI 210(e.g., over a section of the segmented image 200). The productinformation may include, for example, the reference image 308 of therecognized product, the name 310 of the recognized product, the view 312of the recognized product, the number of facings 314 (e.g., including anumber of rows and columns of the facings), the stock information 320,the pallet information 322, the metric information 324, the priceinformation 326 related to the recognized product and/or the range 328of the price of the recognized product. In other examples, other productinformation may be displayed. The quick preview window 300 displays(e.g., presents) product information relating to the matched productassigned to the product 108 g in the corresponding frame 202 g and/orROI 210. Additionally or alternatively, the quick preview window 300 maydisplay candidate products. In some examples, the candidate products 304a-304 n may be obtained via the candidate product searcher 130, forexample. In other examples, the candidate products are based onreference products identified during the automatic image recognitionprocess (at block 802) as potentially matching the first product (e.g.,as in the segmented image 400 of FIG. 4). In some examples, thecandidate products are obtained using a string comparison algorithm andsorted by frequency of appearance. For example, if information about theautomatic image recognition process is not provided, a string comparisonmay be used to identify candidate products. For instance, if theassigned product is “COCA COLA 500ML,” the auditing system 100 maysearch the product database 114 for products containing “COCA,” “COLA,”and/or “500ML,” and the results (e.g., the candidate products bearingthese terms) may be sorted by frequency of appearance (e.g., from mostfrequent reference product to least frequent). Additionally, in someexamples, characteristic information of the store (e.g., the retailstore or merchant) may be used to filter reference products (e.g. baseda product category, such as beers, fruit juices, etc.).

At block 810, the example method 800 includes determining whether theproduct information is correctly identified. If the product informationis correct, the example method 800 includes determining whether there isanother product to review at block 812. Otherwise, if the productinformation is not correctly identified, at block 814 the example method800 includes changing (e.g., modifying, adjusting, alternating, etc.)the product information. For example, if the view information 312 isincorrectly identified, the auditor may change the view information 312in the quick preview window 300 (e.g., by selecting another view from adropdown menu).

In some examples, the quick preview window 300 displays the metricinformation 324, which may include instructions or warnings regardingpotential errors in the product information. For example, the auditingsystem 100 may analyze the options or information chosen by the auditorand determine if there are any potential errors associated with productinformation. If so, warnings may be displayed to indicate the potentialerrors in the product information.

At block 816, the example auditing system 100 updates the product recordin the match database 120 for the product identified in the segmentedimage 200. In some examples, the example report generator 124 generatesthe report 126 (e.g., an audit) of the products (and relatedinformation) identified in the captured image 104. At block 812, theauditor determines if there are more products/results to review in thesegmented image. If so, the example method 800 advances to block 806again. This process may be repeated any number of times (e.g., until allproducts are correctly identified in the segmented image 300). Theexample method 800 results in a consistent quality and accuracy of theaudits performed by auditors.

FIG. 9 is a block diagram of an example processor platform 900 capableof executing instructions of FIGS. 6, 7 and 8 to implement the examplesystem 100 of FIG. 1. The processor platform 900 can be, for example, aserver, a personal computer, a mobile device (e.g., a cell phone, asmart phone, a tablet such as an iPad™), a personal digital assistant(PDA), an Internet appliance, a DVD player, a CD player, a digital videorecorder, a Blu-ray player, a gaming console, a personal video recorder,a set top box, or any other type of computing device.

The processor platform 900 of the illustrated example includes aprocessor 912. The processor 912 of the illustrated example is hardware.For example, the processor 912 can be implemented by one or moreintegrated circuits, logic circuits, microprocessors or controllers fromany desired family or manufacturer.

The processor 912 of the illustrated example includes a local memory 913(e.g., a cache). The processor 912 of the illustrated example is incommunication with a main memory including a volatile memory 914 and anon-volatile memory 916 via a bus 918. The volatile memory 914 may beimplemented by Synchronous Dynamic Random Access Memory (SDRAM), DynamicRandom Access Memory (DRAM), RAMBUS Dynamic Random Access Memory (RDRAM)and/or any other type of random access memory device. The non-volatilememory 916 may be implemented by flash memory and/or any other desiredtype of memory device. Access to the main memory 914, 916 is controlledby a memory controller.

The processor platform 900 of the illustrated example also includes aninterface circuit 920. The interface circuit 920 may be implemented byany type of interface standard, such as an Ethernet interface, auniversal serial bus (USB), and/or a PCI express interface.

In the illustrated example, one or more input devices 922 are connectedto the interface circuit 920. The input device(s) 922 permit(s) a userto enter data and commands into the processor 912. The input device(s)can be implemented by, for example, an audio sensor, a microphone, acamera (still or video), a keyboard, a button, a mouse, a touchscreen, atrack-pad, a trackball, isopoint and/or a voice recognition system.

One or more output devices 924 are also connected to the interfacecircuit 920 of the illustrated example. The output devices 924 can beimplemented, for example, by display devices (e.g., a light emittingdiode (LED), an organic light emitting diode (OLED), a liquid crystaldisplay, a cathode ray tube display (CRT), a touchscreen, a tactileoutput device, a printer and/or speakers). The interface circuit 920 ofthe illustrated example, thus, typically includes a graphics drivercard, a graphics driver chip or a graphics driver processor.

The interface circuit 920 of the illustrated example also includes acommunication device such as a transmitter, a receiver, a transceiver, amodem and/or network interface card to facilitate exchange of data withexternal machines (e.g., computing devices of any kind) via a network926 (e.g., an Ethernet connection, a digital subscriber line (DSL), atelephone line, coaxial cable, a cellular telephone system, etc.).

The processor platform 900 of the illustrated example also includes oneor more mass storage devices 928 for storing software and/or data.Examples of such mass storage devices 928 include floppy disk drives,hard drive disks, compact disk drives, Blu-ray disk drives, RAIDsystems, and digital versatile disk (DVD) drives.

The coded instructions 932 of FIGS. 6, 7 and 8 may be stored in the massstorage device 928, in the volatile memory 914, in the non-volatilememory 916, and/or on a removable tangible computer readable storagemedium such as a CD or DVD.

FIG. 10 is a block diagram of an example processor platform 1000 capableof executing instructions of FIG. 7 to implement the example candidateproduct searcher 130 of FIGS. 1 and 5. The processor platform 1000 canbe, for example, a server, a personal computer, a mobile device (e.g., acell phone, a smart phone, a tablet such as an iPad™), a personaldigital assistant (PDA), an Internet appliance, a DVD player, a CDplayer, a digital video recorder, a Blu-ray player, a gaming console, apersonal video recorder, a set top box, or any other type of computingdevice.

The processor platform 1000 of the illustrated example includes aprocessor 1012. The processor 1012 of the illustrated example ishardware. For example, the processor 1012 can be implemented by one ormore integrated circuits, logic circuits, microprocessors or controllersfrom any desired family or manufacturer.

The processor 1012 of the illustrated example includes a local memory1013 (e.g., a cache). The processor 1012 of the illustrated example isin communication with a main memory including a volatile memory 1014 anda non-volatile memory 1016 via a bus 1018. The volatile memory 1014 maybe implemented by Synchronous Dynamic Random Access Memory (SDRAM),Dynamic Random Access Memory (DRAM), RAMBUS Dynamic Random Access Memory(RDRAM) and/or any other type of random access memory device. Thenon-volatile memory 1016 may be implemented by flash memory and/or anyother desired type of memory device. Access to the main memory 1014,1016 is controlled by a memory controller.

The processor platform 1000 of the illustrated example also includes aninterface circuit 1020. The interface circuit 1020 may be implemented byany type of interface standard, such as an Ethernet interface, auniversal serial bus (USB), and/or a PCI express interface.

In the illustrated example, one or more input devices 1022 are connectedto the interface circuit 1020. The input device(s) 1022 permit(s) a userto enter data and commands into the processor 1012. The input device(s)can be implemented by, for example, an audio sensor, a microphone, acamera (still or video), a keyboard, a button, a mouse, a touchscreen, atrack-pad, a trackball, isopoint and/or a voice recognition system.

One or more output devices 1024 are also connected to the interfacecircuit 1020 of the illustrated example. The output devices 1024 can beimplemented, for example, by display devices (e.g., a light emittingdiode (LED), an organic light emitting diode (OLED), a liquid crystaldisplay, a cathode ray tube display (CRT), a touchscreen, a tactileoutput device, a printer and/or speakers). The interface circuit 1020 ofthe illustrated example, thus, typically includes a graphics drivercard, a graphics driver chip or a graphics driver processor.

The interface circuit 1020 of the illustrated example also includes acommunication device such as a transmitter, a receiver, a transceiver, amodem and/or network interface card to facilitate exchange of data withexternal machines (e.g., computing devices of any kind) via a network1026 (e.g., an Ethernet connection, a digital subscriber line (DSL), atelephone line, coaxial cable, a cellular telephone system, etc.).

The processor platform 1000 of the illustrated example also includes oneor more mass storage devices 1028 for storing software and/or data.Examples of such mass storage devices 1028 include floppy disk drives,hard drive disks, compact disk drives, Blu-ray disk drives, RAIDsystems, and digital versatile disk (DVD) drives.

The coded instructions 1032 of FIG. 7 may be stored in the mass storagedevice 1028, in the volatile memory 1014, in the non-volatile memory1016, and/or on a removable tangible computer readable storage mediumsuch as a CD or DVD.

From the foregoing, it will be appreciated that the above disclosedmethods, apparatus/systems and articles of manufacture enable an auditoror operator to more quickly and efficiently perform an audit of apoint-of-sale image. The examples disclosed herein may be used toperform a confidence search of an ROI and present a plurality ofcandidate products to the auditor, thereby allowing the auditor to morequickly and accurately (e.g., and with more confidence) select acandidate product as corresponding to a product in the ROI. The examplesdisclosed herein also enable an auditor to view and modify productinformation related to the segments or frames of a segmentation producedduring an automatic image recognition process, further reducing the timespent by an auditor performing an audit of a point-of-sale image. As aresult, the examples disclosed herein produce higher quality data.

Although certain example methods, apparatus and articles of manufacturehave been disclosed herein, the scope of coverage of this patent is notlimited thereto. On the contrary, this patent covers all methods,apparatus and articles of manufacture fairly falling within the scope ofthe claims of this patent.

1. A method comprising: comparing, with a processor, a region ofinterest of a first image displayed via a user interface with aplurality of reference product images stored in a database to identify aplurality of candidate product images from the plurality of referenceproduct images as potential matches to a first product depicted in thefirst image, the candidate product images being associated withrespective confidence levels indicating respective likelihoods ofmatching the first product; displaying, via the user interface, thecandidate product images simultaneously with the first image in a mannerbased on the respective confidence levels; and automatically selecting,with the processor, a first one of the candidate product images asmatching the first product based on the respective confidence levels. 2.The method of claim 1, wherein the comparing of the region of interestwith the plurality of reference product images includes: detecting firstcorners of the region of interest; extracting first features from thedetected first corners of the region of interest; detecting secondcorners of a first reference product image of the plurality of referenceproduct images; extracting second features from the detected secondcorners; determining relations between the first features and the secondfeatures; and determining a dominant transformation projection betweenthe region of interest and the first reference product image based onthe relations.
 3. (canceled)
 4. (canceled)
 5. The method of claim 2,further including: determining a number of inliers of the first featuresbased the dominant transformation projection; and determining a firstone of the respective confidence levels associated with the firstreference product image based on the number of the inliers. 6.(canceled)
 7. The method of claim 1, wherein the first image is asegmented image including one or more frames corresponding to respectiveproducts identified in the first image with an automatic imagerecognition process, the one or more frames including respective productnames overlaying the respective products identified in the respectiveframes.
 8. (canceled)
 9. (canceled)
 10. The method of claim 1, whereinthe candidate product images are displayed in order of the respectiveconfidence levels.
 11. The method of claim 1, further includingdisplaying values representative of the respective confidence levelsadjacent to the candidate product images.
 12. The method of claim 1,further including displaying the first one of the candidate productimages in a manner indicating the first one of the candidate productimages has been selected as being matched to the first product.
 13. Anapparatus comprising: a product database storing a plurality ofreference product images; a candidate product searcher to: compare aregion of interest of a first image with the plurality of referenceproduct images to identify a plurality of candidate product images fromthe plurality of reference product images as potential matches to afirst product depicted in the first image, the plurality of candidateproduct images being associated with respective confidence levelsindicating respective likelihoods of matching the first product; andautomatically select a first one of the candidate product images asmatching the first product based on the respective confidence levels;and a user interface to display the first image and the candidateproduct images simultaneously with the first image in a manner based onthe respective confidence levels.
 14. The apparatus of claim 13,wherein, to compare the region of interest with the plurality ofreference product images, the candidate product searcher is to: detectfirst corners of the region of interest; extract first features from thedetected first corners of the region of interest; detect second cornersof a first reference product image of the plurality of reference productimages; extract second features from the detected second corners;determine relations between the first features and the second features;and determine a dominant transformation projection between the region ofinterest and the first reference product image based on the relations.15. (canceled)
 16. (canceled)
 17. The apparatus of claim 14, wherein thecandidate product searcher is further to: determine a number of inliersof the first features based the dominant transformation projection; anddetermine a first one of the respective confidence levels associatedwith the first reference product image based on the number of theinliers.
 18. The apparatus of claim 13, wherein, to compare the regionof interest of the first image with the plurality of reference productimages to identify the plurality of candidate product images, thecandidate product searcher is to: assign the respective confidencelevels to the reference product images; and compare the respectiveconfidence levels associated with the reference product images to athreshold. 19-21. (canceled)
 22. The apparatus of claim 13, wherein thecandidate product images are displayed in order of the respectiveconfidence levels.
 23. The apparatus of claim 13, wherein the userinterface is further to display values representative of the respectiveconfidence levels adjacent to the candidate product images. 24.(canceled)
 25. A tangible machine readable storage medium comprisinginstructions that, when executed, cause a machine to at least: compare aregion of interest of a first image displayed via a user interface witha plurality of reference product images stored in a database to identifya plurality of candidate product images from the plurality of referenceproduct images as potential matches to a first product depicted in thefirst image, the candidate product images being associated withrespective confidence levels indicating respective likelihoods ofmatching the first product; display, via the user interface, thecandidate product images simultaneously with the first image in a mannerbased on the respective confidence levels; and automatically select afirst one of the candidate product images as matching the first productbased on the respective confidence levels.
 26. The tangible machinereadable storage medium of claim 25, wherein, to compare the region ofinterest with the plurality of reference product images, theinstructions, when executed, cause the machine to: detect first cornersof the region of interest; extract first features from the detectedfirst corners of the region of interest; detect second corners of afirst reference product image of the plurality of reference productimages; extract second features from the detected second corners;determine relations between the first features and the second features;and determine a dominant transformation projection between the region ofinterest and the first reference product image based on the relations.27. (canceled)
 28. (canceled)
 29. The tangible machine readable storagemedium of claim 26, wherein the instructions, when executed, furthercause the machine to: determine a number of inliers of the firstfeatures based the dominant transformation projection; and determine afirst one of the respective confidence levels associated with the firstreference product image based on the number of the inliers. 30.(canceled)
 31. (canceled)
 32. The tangible machine readable storagemedium of claim 25, wherein the instructions, when executed, furthercause the machine to at least one of create a record or update therecord to indicate the first product matched the first one of thecandidate product images.
 33. (canceled)
 34. The tangible machinereadable storage medium of claim 25, wherein the candidate productimages are displayed in order of the respective confidence levels. 35.The tangible machine readable storage medium of claim 25, wherein theinstructions, when executed, further cause the machine to display, viathe user interface, values representative of the respective confidencelevels adjacent to the candidate product images.
 36. The tangiblemachine readable storage medium of claim 25, wherein the instructions,when executed, further cause the machine to display, via the userinterface, the first one of the candidate product images in a mannerindicating the first one of the candidate product images has beenselected as being matched to the first product. 37-60. (canceled)